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<title>Doxygen: pcl::face_detection::PoseClassRegressionVarianceStatsEstimator&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt; 模板类 参考</title>
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<div class="header">
  <div class="summary">
<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pri-attribs">Private 属性</a> &#124;
<a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator-members.html">所有成员列表</a>  </div>
  <div class="headertitle">
<div class="title">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt; 模板类 参考</div>  </div>
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<p><a class="el" href="class_statistics.html">Statistics</a> estimator for regression trees which optimizes information gain and pose parameters error.  
 <a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#details">更多...</a></p>

<p><code>#include &lt;<a class="el" href="rf__face__utils_8h_source.html">rf_face_utils.h</a>&gt;</code></p>
<div class="dynheader">
类 pcl::face_detection::PoseClassRegressionVarianceStatsEstimator&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt; 继承关系图:</div>
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 <div class="center">
  <img src="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.png" usemap="#pcl::face_5Fdetection::PoseClassRegressionVarianceStatsEstimator_3C_20LabelDataType_2C_20NodeType_2C_20DataSet_2C_20ExampleIndex_20_3E_map" alt=""/>
  <map id="pcl::face_5Fdetection::PoseClassRegressionVarianceStatsEstimator_3C_20LabelDataType_2C_20NodeType_2C_20DataSet_2C_20ExampleIndex_20_3E_map" name="pcl::face_5Fdetection::PoseClassRegressionVarianceStatsEstimator_3C_20LabelDataType_2C_20NodeType_2C_20DataSet_2C_20ExampleIndex_20_3E_map">
<area href="classpcl_1_1_stats_estimator.html" title="Class interface for gathering statistics for decision tree learning." alt="pcl::StatsEstimator&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;" shape="rect" coords="0,0,691,24"/>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public 成员函数</h2></td></tr>
<tr class="memitem:a437ef68ac62fe7af67c98d69585dcdc7"><td class="memItemLeft" align="right" valign="top"><a id="a437ef68ac62fe7af67c98d69585dcdc7"></a>
&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a437ef68ac62fe7af67c98d69585dcdc7">PoseClassRegressionVarianceStatsEstimator</a> (<a class="el" href="classpcl_1_1_branch_estimator.html">BranchEstimator</a> *branch_estimator)</td></tr>
<tr class="memdesc:a437ef68ac62fe7af67c98d69585dcdc7"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor. <br /></td></tr>
<tr class="separator:a437ef68ac62fe7af67c98d69585dcdc7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aba50205ec09702af045e369a0d85d849"><td class="memItemLeft" align="right" valign="top"><a id="aba50205ec09702af045e369a0d85d849"></a>
virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#aba50205ec09702af045e369a0d85d849">~PoseClassRegressionVarianceStatsEstimator</a> ()</td></tr>
<tr class="memdesc:aba50205ec09702af045e369a0d85d849"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destructor. <br /></td></tr>
<tr class="separator:aba50205ec09702af045e369a0d85d849"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a33a428f0478566c3b75f2d14c28039b7"><td class="memItemLeft" align="right" valign="top"><a id="a33a428f0478566c3b75f2d14c28039b7"></a>
size_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a33a428f0478566c3b75f2d14c28039b7">getNumOfBranches</a> () const</td></tr>
<tr class="memdesc:a33a428f0478566c3b75f2d14c28039b7"><td class="mdescLeft">&#160;</td><td class="mdescRight">Returns the number of branches the corresponding tree has. <br /></td></tr>
<tr class="separator:a33a428f0478566c3b75f2d14c28039b7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af483f527511dd87173ff5c4dd130340e"><td class="memItemLeft" align="right" valign="top">LabelDataType&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#af483f527511dd87173ff5c4dd130340e">getLabelOfNode</a> (NodeType &amp;node) const</td></tr>
<tr class="memdesc:af483f527511dd87173ff5c4dd130340e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Returns the label of the specified node.  <a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#af483f527511dd87173ff5c4dd130340e">更多...</a><br /></td></tr>
<tr class="separator:af483f527511dd87173ff5c4dd130340e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a67ca30186cf7469d8cfb13f7ed71f51b"><td class="memItemLeft" align="right" valign="top">unsigned int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a67ca30186cf7469d8cfb13f7ed71f51b">computeMeanAndCovarianceOffset</a> (DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, Eigen::Matrix3d &amp;covariance_matrix, Eigen::Vector3d &amp;centroid) const</td></tr>
<tr class="memdesc:a67ca30186cf7469d8cfb13f7ed71f51b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the covariance matrix for translation offsets.  <a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a67ca30186cf7469d8cfb13f7ed71f51b">更多...</a><br /></td></tr>
<tr class="separator:a67ca30186cf7469d8cfb13f7ed71f51b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1ecad7c6e8235d22d9dded01a7b39f9e"><td class="memItemLeft" align="right" valign="top">unsigned int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a1ecad7c6e8235d22d9dded01a7b39f9e">computeMeanAndCovarianceAngles</a> (DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, Eigen::Matrix3d &amp;covariance_matrix, Eigen::Vector3d &amp;centroid) const</td></tr>
<tr class="memdesc:a1ecad7c6e8235d22d9dded01a7b39f9e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the covariance matrix for rotation values.  <a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a1ecad7c6e8235d22d9dded01a7b39f9e">更多...</a><br /></td></tr>
<tr class="separator:a1ecad7c6e8235d22d9dded01a7b39f9e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a99c1a899a7a6336508dc54b4d2445229"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a99c1a899a7a6336508dc54b4d2445229">computeInformationGain</a> (DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, std::vector&lt; LabelDataType &gt; &amp;label_data, std::vector&lt; float &gt; &amp;results, std::vector&lt; unsigned char &gt; &amp;flags, const float threshold) const</td></tr>
<tr class="memdesc:a99c1a899a7a6336508dc54b4d2445229"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the information gain obtained by the specified threshold.  <a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a99c1a899a7a6336508dc54b4d2445229">更多...</a><br /></td></tr>
<tr class="separator:a99c1a899a7a6336508dc54b4d2445229"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a4bd8fe3020b0adb041b68bcb01477dd8"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a4bd8fe3020b0adb041b68bcb01477dd8">computeBranchIndices</a> (std::vector&lt; float &gt; &amp;results, std::vector&lt; unsigned char &gt; &amp;flags, const float threshold, std::vector&lt; unsigned char &gt; &amp;branch_indices) const</td></tr>
<tr class="memdesc:a4bd8fe3020b0adb041b68bcb01477dd8"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the branch indices for all supplied results.  <a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a4bd8fe3020b0adb041b68bcb01477dd8">更多...</a><br /></td></tr>
<tr class="separator:a4bd8fe3020b0adb041b68bcb01477dd8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a5f5d4b1e73621420129b70c12f7b375a"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a5f5d4b1e73621420129b70c12f7b375a">computeBranchIndex</a> (const float result, const unsigned char flag, const float threshold, unsigned char &amp;branch_index) const</td></tr>
<tr class="memdesc:a5f5d4b1e73621420129b70c12f7b375a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the branch index for the specified result.  <a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a5f5d4b1e73621420129b70c12f7b375a">更多...</a><br /></td></tr>
<tr class="separator:a5f5d4b1e73621420129b70c12f7b375a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa27bf41909f401c9aaaea96693eba5fb"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#aa27bf41909f401c9aaaea96693eba5fb">computeAndSetNodeStats</a> (DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, std::vector&lt; LabelDataType &gt; &amp;label_data, NodeType &amp;node) const</td></tr>
<tr class="memdesc:aa27bf41909f401c9aaaea96693eba5fb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes and sets the statistics for a node.  <a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#aa27bf41909f401c9aaaea96693eba5fb">更多...</a><br /></td></tr>
<tr class="separator:aa27bf41909f401c9aaaea96693eba5fb"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a965ac0738f571aae26a3f52ccbf26f8f"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a965ac0738f571aae26a3f52ccbf26f8f">generateCodeForBranchIndexComputation</a> (NodeType &amp;, std::ostream &amp;stream) const</td></tr>
<tr class="memdesc:a965ac0738f571aae26a3f52ccbf26f8f"><td class="mdescLeft">&#160;</td><td class="mdescRight">Generates code for branch index computation.  <a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a965ac0738f571aae26a3f52ccbf26f8f">更多...</a><br /></td></tr>
<tr class="separator:a965ac0738f571aae26a3f52ccbf26f8f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a387f6dafb7ca51c20793fab106b202e1"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a387f6dafb7ca51c20793fab106b202e1">generateCodeForOutput</a> (NodeType &amp;, std::ostream &amp;stream) const</td></tr>
<tr class="memdesc:a387f6dafb7ca51c20793fab106b202e1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Generates code for label output.  <a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a387f6dafb7ca51c20793fab106b202e1">更多...</a><br /></td></tr>
<tr class="separator:a387f6dafb7ca51c20793fab106b202e1"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_methods_classpcl_1_1_stats_estimator"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classpcl_1_1_stats_estimator')"><img src="closed.png" alt="-"/>&#160;Public 成员函数 继承自 <a class="el" href="classpcl_1_1_stats_estimator.html">pcl::StatsEstimator&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;</a></td></tr>
<tr class="memitem:aa3ca14fa2a1a43861a0278370b165203 inherit pub_methods_classpcl_1_1_stats_estimator"><td class="memItemLeft" align="right" valign="top"><a id="aa3ca14fa2a1a43861a0278370b165203"></a>
virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_stats_estimator.html#aa3ca14fa2a1a43861a0278370b165203">~StatsEstimator</a> ()</td></tr>
<tr class="memdesc:aa3ca14fa2a1a43861a0278370b165203 inherit pub_methods_classpcl_1_1_stats_estimator"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destructor. <br /></td></tr>
<tr class="separator:aa3ca14fa2a1a43861a0278370b165203 inherit pub_methods_classpcl_1_1_stats_estimator"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-attribs"></a>
Private 属性</h2></td></tr>
<tr class="memitem:a6a6bd626223fb5bd6b3cb9b9d6aeea55"><td class="memItemLeft" align="right" valign="top"><a id="a6a6bd626223fb5bd6b3cb9b9d6aeea55"></a>
<a class="el" href="classpcl_1_1_branch_estimator.html">pcl::BranchEstimator</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a6a6bd626223fb5bd6b3cb9b9d6aeea55">branch_estimator_</a></td></tr>
<tr class="memdesc:a6a6bd626223fb5bd6b3cb9b9d6aeea55"><td class="mdescLeft">&#160;</td><td class="mdescRight">The branch estimator. <br /></td></tr>
<tr class="separator:a6a6bd626223fb5bd6b3cb9b9d6aeea55"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><h3>template&lt;class LabelDataType, class NodeType, class DataSet, class ExampleIndex&gt;<br />
class pcl::face_detection::PoseClassRegressionVarianceStatsEstimator&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;</h3>

<p><a class="el" href="class_statistics.html">Statistics</a> estimator for regression trees which optimizes information gain and pose parameters error. </p>
</div><h2 class="groupheader">成员函数说明</h2>
<a id="aa27bf41909f401c9aaaea96693eba5fb"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aa27bf41909f401c9aaaea96693eba5fb">&#9670;&nbsp;</a></span>computeAndSetNodeStats()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;class LabelDataType , class NodeType , class DataSet , class ExampleIndex &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator</a>&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;::computeAndSetNodeStats </td>
          <td>(</td>
          <td class="paramtype">DataSet &amp;&#160;</td>
          <td class="paramname"><em>data_set</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; ExampleIndex &gt; &amp;&#160;</td>
          <td class="paramname"><em>examples</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; LabelDataType &gt; &amp;&#160;</td>
          <td class="paramname"><em>label_data</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">NodeType &amp;&#160;</td>
          <td class="paramname"><em>node</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Computes and sets the statistics for a node. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">data_set</td><td>The data set which is evaluated. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">examples</td><td>The examples which define which parts of the data set are used for evaluation. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">label_data</td><td>The label_data corresponding to the examples. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">node</td><td>The destination node for the statistics. </td></tr>
  </table>
  </dd>
</dl>

<p>实现了 <a class="el" href="classpcl_1_1_stats_estimator.html#a6caa1bf87f7cb0b697d4fc081f0339af">pcl::StatsEstimator&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;</a>.</p>
<div class="fragment"><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;        {</div>
<div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_examples = examples.size ();</div>
<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160; </div>
<div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;          LabelDataType sum = 0.0f;</div>
<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;          LabelDataType sqr_sum = 0.0f;</div>
<div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> example_index = 0; example_index &lt; num_of_examples; ++example_index)</div>
<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;          {</div>
<div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;            <span class="keyword">const</span> LabelDataType label = label_data[example_index];</div>
<div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160; </div>
<div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;            sum += label;</div>
<div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;            sqr_sum += label * label;</div>
<div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;          }</div>
<div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160; </div>
<div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;          sum /= <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(num_of_examples);</div>
<div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;          sqr_sum /= <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(num_of_examples);</div>
<div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160; </div>
<div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">float</span> variance = sqr_sum - sum * sum;</div>
<div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160; </div>
<div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;          node.value = sum;</div>
<div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;          node.variance = variance;</div>
<div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160; </div>
<div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;          <span class="comment">//set node stats regarding pose regression</span></div>
<div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;          std::vector &lt; ExampleIndex &gt; positive_examples;</div>
<div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160; </div>
<div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> example_index = 0; example_index &lt; num_of_examples; ++example_index)</div>
<div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;          {</div>
<div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;            LabelDataType label = label_data[example_index];</div>
<div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160; </div>
<div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;            <span class="keywordflow">if</span> (label == 1)</div>
<div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;              positive_examples.push_back (examples[example_index]);</div>
<div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160; </div>
<div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;          }</div>
<div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160; </div>
<div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;          <span class="comment">//compute covariance from offsets and angles</span></div>
<div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;          <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a67ca30186cf7469d8cfb13f7ed71f51b">computeMeanAndCovarianceOffset</a> (data_set, positive_examples, node.covariance_trans_, node.trans_mean_);</div>
<div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;          <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a1ecad7c6e8235d22d9dded01a7b39f9e">computeMeanAndCovarianceAngles</a> (data_set, positive_examples, node.covariance_rot_, node.rot_mean_);</div>
<div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;        }</div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a1ecad7c6e8235d22d9dded01a7b39f9e"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a1ecad7c6e8235d22d9dded01a7b39f9e">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::computeMeanAndCovarianceAngles</a></div><div class="ttdeci">unsigned int computeMeanAndCovarianceAngles(DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, Eigen::Matrix3d &amp;covariance_matrix, Eigen::Vector3d &amp;centroid) const</div><div class="ttdoc">Computes the covariance matrix for rotation values.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:276</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a67ca30186cf7469d8cfb13f7ed71f51b"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a67ca30186cf7469d8cfb13f7ed71f51b">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::computeMeanAndCovarianceOffset</a></div><div class="ttdeci">unsigned int computeMeanAndCovarianceOffset(DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, Eigen::Matrix3d &amp;covariance_matrix, Eigen::Vector3d &amp;centroid) const</div><div class="ttdoc">Computes the covariance matrix for translation offsets.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:232</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a5f5d4b1e73621420129b70c12f7b375a">&#9670;&nbsp;</a></span>computeBranchIndex()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;class LabelDataType , class NodeType , class DataSet , class ExampleIndex &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
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          <td class="memname">void <a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator</a>&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;::computeBranchIndex </td>
          <td>(</td>
          <td class="paramtype">const float&#160;</td>
          <td class="paramname"><em>result</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const unsigned char&#160;</td>
          <td class="paramname"><em>flag</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const float&#160;</td>
          <td class="paramname"><em>threshold</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">unsigned char &amp;&#160;</td>
          <td class="paramname"><em>branch_index</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
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<p>Computes the branch index for the specified result. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">result</td><td>The result the branch index will be computed for. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">flag</td><td>The flag corresponding to the specified result. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">threshold</td><td>The threshold used to compute the branch index. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">branch_index</td><td>The destination for the computed branch index. </td></tr>
  </table>
  </dd>
</dl>

<p>实现了 <a class="el" href="classpcl_1_1_stats_estimator.html#ae91303f940dffa974c8f980fa8736426">pcl::StatsEstimator&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;</a>.</p>
<div class="fragment"><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;        {</div>
<div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;          <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a6a6bd626223fb5bd6b3cb9b9d6aeea55">branch_estimator_</a>-&gt;<a class="code" href="classpcl_1_1_branch_estimator.html#a595a4e2ddc742910336912ff66c6feba">computeBranchIndex</a> (result, flag, threshold, branch_index);</div>
<div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;        }</div>
<div class="ttc" id="aclasspcl_1_1_branch_estimator_html_a595a4e2ddc742910336912ff66c6feba"><div class="ttname"><a href="classpcl_1_1_branch_estimator.html#a595a4e2ddc742910336912ff66c6feba">pcl::BranchEstimator::computeBranchIndex</a></div><div class="ttdeci">virtual void computeBranchIndex(const float result, const unsigned char flag, const float threshold, unsigned char &amp;branch_index) const =0</div><div class="ttdoc">Computes the branch index for the specified result.</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a6a6bd626223fb5bd6b3cb9b9d6aeea55"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a6a6bd626223fb5bd6b3cb9b9d6aeea55">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::branch_estimator_</a></div><div class="ttdeci">pcl::BranchEstimator * branch_estimator_</div><div class="ttdoc">The branch estimator.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:535</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a4bd8fe3020b0adb041b68bcb01477dd8">&#9670;&nbsp;</a></span>computeBranchIndices()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;class LabelDataType , class NodeType , class DataSet , class ExampleIndex &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator</a>&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;::computeBranchIndices </td>
          <td>(</td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>results</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; unsigned char &gt; &amp;&#160;</td>
          <td class="paramname"><em>flags</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const float&#160;</td>
          <td class="paramname"><em>threshold</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; unsigned char &gt; &amp;&#160;</td>
          <td class="paramname"><em>branch_indices</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Computes the branch indices for all supplied results. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">results</td><td>The results the branch indices will be computed for. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">flags</td><td>The flags corresponding to the specified results. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">threshold</td><td>The threshold used to compute the branch indices. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">branch_indices</td><td>The destination for the computed branch indices. </td></tr>
  </table>
  </dd>
</dl>

<p>实现了 <a class="el" href="classpcl_1_1_stats_estimator.html#aa3fda8a830fbaded719ac28b2d6667bb">pcl::StatsEstimator&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;</a>.</p>
<div class="fragment"><div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;        {</div>
<div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_results = results.size ();</div>
<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160; </div>
<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;          branch_indices.resize (num_of_results);</div>
<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> result_index = 0; result_index &lt; num_of_results; ++result_index)</div>
<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;          {</div>
<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;            <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span> branch_index;</div>
<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;            <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a5f5d4b1e73621420129b70c12f7b375a">computeBranchIndex</a> (results[result_index], flags[result_index], threshold, branch_index);</div>
<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;            branch_indices[result_index] = branch_index;</div>
<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;          }</div>
<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;        }</div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a5f5d4b1e73621420129b70c12f7b375a"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a5f5d4b1e73621420129b70c12f7b375a">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::computeBranchIndex</a></div><div class="ttdeci">void computeBranchIndex(const float result, const unsigned char flag, const float threshold, unsigned char &amp;branch_index) const</div><div class="ttdoc">Computes the branch index for the specified result.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:465</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a99c1a899a7a6336508dc54b4d2445229">&#9670;&nbsp;</a></span>computeInformationGain()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;class LabelDataType , class NodeType , class DataSet , class ExampleIndex &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">float <a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator</a>&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;::computeInformationGain </td>
          <td>(</td>
          <td class="paramtype">DataSet &amp;&#160;</td>
          <td class="paramname"><em>data_set</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; ExampleIndex &gt; &amp;&#160;</td>
          <td class="paramname"><em>examples</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; LabelDataType &gt; &amp;&#160;</td>
          <td class="paramname"><em>label_data</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>results</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; unsigned char &gt; &amp;&#160;</td>
          <td class="paramname"><em>flags</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const float&#160;</td>
          <td class="paramname"><em>threshold</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Computes the information gain obtained by the specified threshold. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">data_set</td><td>The data set corresponding to the supplied result data. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">examples</td><td>The examples used for extracting the supplied result data. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">label_data</td><td>The label data corresponding to the specified examples. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">results</td><td>The results computed using the specified examples. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">flags</td><td>The flags corresponding to the results. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">threshold</td><td>The threshold for which the information gain is computed. </td></tr>
  </table>
  </dd>
</dl>

<p>实现了 <a class="el" href="classpcl_1_1_stats_estimator.html#a4794b4417d32e2844bb137ce7934f905">pcl::StatsEstimator&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;</a>.</p>
<div class="fragment"><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;        {</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_examples = examples.size ();</div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_branches = <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a33a428f0478566c3b75f2d14c28039b7">getNumOfBranches</a> ();</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160; </div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;          <span class="comment">// compute variance</span></div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;          std::vector &lt; LabelDataType &gt; sums (num_of_branches + 1, 0.f);</div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;          std::vector &lt; LabelDataType &gt; sqr_sums (num_of_branches + 1, 0.f);</div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;          std::vector &lt; size_t &gt; branch_element_count (num_of_branches + 1, 0.f);</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160; </div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; num_of_branches; ++branch_index)</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;          {</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;            branch_element_count[branch_index] = 1;</div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;            ++branch_element_count[num_of_branches];</div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;          }</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160; </div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> example_index = 0; example_index &lt; num_of_examples; ++example_index)</div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;          {</div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;            <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span> branch_index;</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;            <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a5f5d4b1e73621420129b70c12f7b375a">computeBranchIndex</a> (results[example_index], flags[example_index], threshold, branch_index);</div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160; </div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;            LabelDataType label = label_data[example_index];</div>
<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160; </div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;            ++branch_element_count[branch_index];</div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;            ++branch_element_count[num_of_branches];</div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160; </div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;            sums[branch_index] += label;</div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;            sums[num_of_branches] += label;</div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;          }</div>
<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160; </div>
<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;          std::vector&lt;float&gt; hp (num_of_branches + 1, 0.f);</div>
<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; (num_of_branches + 1); ++branch_index)</div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;          {</div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;            <span class="keywordtype">float</span> pf = sums[branch_index] / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (branch_element_count[branch_index]);</div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;            <span class="keywordtype">float</span> pnf = (<span class="keyword">static_cast&lt;</span>LabelDataType<span class="keyword">&gt;</span>(branch_element_count[branch_index]) - sums[branch_index] + 1.f)</div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;                        / <span class="keyword">static_cast&lt;</span>LabelDataType<span class="keyword">&gt;</span> (branch_element_count[branch_index]);</div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;            hp[branch_index] -= <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(pf * log (pf) + pnf * log (pnf));</div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;          }</div>
<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160; </div>
<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;          <span class="comment">//use mean of the examples as purity</span></div>
<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;          <span class="keywordtype">float</span> purity = sums[num_of_branches] / <span class="keyword">static_cast&lt;</span>LabelDataType<span class="keyword">&gt;</span>(branch_element_count[num_of_branches]);</div>
<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;          <span class="keywordtype">float</span> tp = 0.8f;</div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160; </div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;          <span class="keywordflow">if</span> (purity &gt;= tp)</div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;          {</div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;            <span class="comment">//compute covariance matrices from translation offsets and angles for the whole set and children</span></div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;            <span class="comment">//consider only positive examples...</span></div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;            std::vector &lt; size_t &gt; branch_element_count (num_of_branches + 1, 0);</div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;            std::vector &lt; std::vector&lt;ExampleIndex&gt; &gt; positive_examples;</div>
<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;            positive_examples.resize (num_of_branches + 1);</div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160; </div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;            <span class="keywordtype">size_t</span> pos = 0;</div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> example_index = 0; example_index &lt; num_of_examples; ++example_index)</div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;            {</div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;              <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span> branch_index;</div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;              <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a5f5d4b1e73621420129b70c12f7b375a">computeBranchIndex</a> (results[example_index], flags[example_index], threshold, branch_index);</div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160; </div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;              LabelDataType label = label_data[example_index];</div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160; </div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;              <span class="keywordflow">if</span> (label == 1 <span class="comment">/*&amp;&amp; !flags[example_index]*/</span>)</div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;              {</div>
<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;                ++branch_element_count[branch_index];</div>
<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;                ++branch_element_count[num_of_branches];</div>
<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160; </div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;                positive_examples[branch_index].push_back (examples[example_index]);</div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;                positive_examples[num_of_branches].push_back (examples[example_index]);</div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;                pos++;</div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;              }</div>
<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;            }</div>
<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160; </div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;            <span class="comment">//compute covariance from offsets and angles for all branchs</span></div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;            std::vector &lt; Eigen::Matrix3d &gt; offset_covariances;</div>
<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;            std::vector &lt; Eigen::Matrix3d &gt; angle_covariances;</div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160; </div>
<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;            std::vector &lt; Eigen::Vector3d &gt; offset_centroids;</div>
<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;            std::vector &lt; Eigen::Vector3d &gt; angle_centroids;</div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160; </div>
<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;            offset_covariances.resize (num_of_branches + 1);</div>
<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;            angle_covariances.resize (num_of_branches + 1);</div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;            offset_centroids.resize (num_of_branches + 1);</div>
<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;            angle_centroids.resize (num_of_branches + 1);</div>
<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160; </div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; (num_of_branches + 1); ++branch_index)</div>
<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;            {</div>
<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;              <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a67ca30186cf7469d8cfb13f7ed71f51b">computeMeanAndCovarianceOffset</a> (data_set, positive_examples[branch_index], offset_covariances[branch_index],</div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;                  offset_centroids[branch_index]);</div>
<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;              <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a1ecad7c6e8235d22d9dded01a7b39f9e">computeMeanAndCovarianceAngles</a> (data_set, positive_examples[branch_index], angle_covariances[branch_index],</div>
<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;                  angle_centroids[branch_index]);</div>
<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;            }</div>
<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160; </div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;            <span class="comment">//update information_gain</span></div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;            std::vector&lt;float&gt; hr (num_of_branches + 1, 0.f);</div>
<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; (num_of_branches + 1); ++branch_index)</div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;            {</div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;              hr[branch_index] = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(0.5f * log (std::pow (2 * M_PI, 3)</div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;                                                    * offset_covariances[branch_index].determinant ())</div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;                                                    + 0.5f * log (std::pow (2 * M_PI, 3)</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;                                                    * angle_covariances[branch_index].determinant ()));</div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;            }</div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160; </div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; (num_of_branches + 1); ++branch_index)</div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;            {</div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;              hp[branch_index] += std::max (sums[branch_index] / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (branch_element_count[branch_index]) - tp, 0.f) * hr[branch_index];</div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;            }</div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;          }</div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160; </div>
<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;          <span class="keywordtype">float</span> information_gain = hp[num_of_branches + 1];</div>
<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; (num_of_branches); ++branch_index)</div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;          {</div>
<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;            information_gain -= <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (branch_element_count[branch_index]) / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (branch_element_count[num_of_branches])</div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;                * hp[branch_index];</div>
<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;          }</div>
<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160; </div>
<div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;          <span class="keywordflow">return</span> information_gain;</div>
<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;        }</div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a33a428f0478566c3b75f2d14c28039b7"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a33a428f0478566c3b75f2d14c28039b7">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::getNumOfBranches</a></div><div class="ttdeci">size_t getNumOfBranches() const</div><div class="ttdoc">Returns the number of branches the corresponding tree has.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:213</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a1ecad7c6e8235d22d9dded01a7b39f9e">&#9670;&nbsp;</a></span>computeMeanAndCovarianceAngles()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;class LabelDataType , class NodeType , class DataSet , class ExampleIndex &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">unsigned int <a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator</a>&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;::computeMeanAndCovarianceAngles </td>
          <td>(</td>
          <td class="paramtype">DataSet &amp;&#160;</td>
          <td class="paramname"><em>data_set</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; ExampleIndex &gt; &amp;&#160;</td>
          <td class="paramname"><em>examples</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">Eigen::Matrix3d &amp;&#160;</td>
          <td class="paramname"><em>covariance_matrix</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">Eigen::Vector3d &amp;&#160;</td>
          <td class="paramname"><em>centroid</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
  </tr>
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</div><div class="memdoc">

<p>Computes the covariance matrix for rotation values. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">data_set</td><td>The corresponding data set. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">examples</td><td>A set of examples from the dataset. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">covariance_matrix</td><td>The covariance matrix. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">centroid</td><td>The mean of the data. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;        {</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;          Eigen::Matrix&lt;double, 1, 9, Eigen::RowMajor&gt; accu = Eigen::Matrix&lt;double, 1, 9, Eigen::RowMajor&gt;::Zero ();</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;          <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> point_count = <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span> (examples.size ());</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160; </div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; point_count; ++i)</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;          {</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;            TrainingExample te = data_set[examples[i]];</div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;            accu[0] += te.rot_[0] * te.rot_[0];</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;            accu[1] += te.rot_[0] * te.rot_[1];</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;            accu[2] += te.rot_[0] * te.rot_[2];</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;            accu[3] += te.rot_[1] * te.rot_[1];</div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;            accu[4] += te.rot_[1] * te.rot_[2];</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;            accu[5] += te.rot_[2] * te.rot_[2];</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;            accu[6] += te.rot_[0];</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;            accu[7] += te.rot_[1];</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;            accu[8] += te.rot_[2];</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;          }</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160; </div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;          <span class="keywordflow">if</span> (point_count != 0)</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;          {</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;            accu /= <span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span> (point_count);</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;            centroid.head&lt;3&gt; ().matrix () = accu.tail&lt;3&gt; ();</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;            covariance_matrix.coeffRef (0) = accu[0] - accu[6] * accu[6];</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;            covariance_matrix.coeffRef (1) = accu[1] - accu[6] * accu[7];</div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;            covariance_matrix.coeffRef (2) = accu[2] - accu[6] * accu[8];</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;            covariance_matrix.coeffRef (4) = accu[3] - accu[7] * accu[7];</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;            covariance_matrix.coeffRef (5) = accu[4] - accu[7] * accu[8];</div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;            covariance_matrix.coeffRef (8) = accu[5] - accu[8] * accu[8];</div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;            covariance_matrix.coeffRef (3) = covariance_matrix.coeff (1);</div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;            covariance_matrix.coeffRef (6) = covariance_matrix.coeff (2);</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;            covariance_matrix.coeffRef (7) = covariance_matrix.coeff (5);</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;          }</div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160; </div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;          <span class="keywordflow">return</span> point_count;</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;        }</div>
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</div>
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<a id="a67ca30186cf7469d8cfb13f7ed71f51b"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a67ca30186cf7469d8cfb13f7ed71f51b">&#9670;&nbsp;</a></span>computeMeanAndCovarianceOffset()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;class LabelDataType , class NodeType , class DataSet , class ExampleIndex &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">unsigned int <a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator</a>&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;::computeMeanAndCovarianceOffset </td>
          <td>(</td>
          <td class="paramtype">DataSet &amp;&#160;</td>
          <td class="paramname"><em>data_set</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; ExampleIndex &gt; &amp;&#160;</td>
          <td class="paramname"><em>examples</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">Eigen::Matrix3d &amp;&#160;</td>
          <td class="paramname"><em>covariance_matrix</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">Eigen::Vector3d &amp;&#160;</td>
          <td class="paramname"><em>centroid</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Computes the covariance matrix for translation offsets. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">data_set</td><td>The corresponding data set. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">examples</td><td>A set of examples from the dataset. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">covariance_matrix</td><td>The covariance matrix. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">centroid</td><td>The mean of the data. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;        {</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;          Eigen::Matrix&lt;double, 1, 9, Eigen::RowMajor&gt; accu = Eigen::Matrix&lt;double, 1, 9, Eigen::RowMajor&gt;::Zero ();</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;          <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> point_count = <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span> (examples.size ());</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160; </div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; point_count; ++i)</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;          {</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;            TrainingExample te = data_set[examples[i]];</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;            accu[0] += te.trans_[0] * te.trans_[0];</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;            accu[1] += te.trans_[0] * te.trans_[1];</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;            accu[2] += te.trans_[0] * te.trans_[2];</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;            accu[3] += te.trans_[1] * te.trans_[1];</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;            accu[4] += te.trans_[1] * te.trans_[2];</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;            accu[5] += te.trans_[2] * te.trans_[2];</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;            accu[6] += te.trans_[0];</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;            accu[7] += te.trans_[1];</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;            accu[8] += te.trans_[2];</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;          }</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160; </div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;          <span class="keywordflow">if</span> (point_count != 0)</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;          {</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;            accu /= <span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span> (point_count);</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;            centroid.head&lt;3&gt; ().matrix () = accu.tail&lt;3&gt; ();</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;            covariance_matrix.coeffRef (0) = accu[0] - accu[6] * accu[6];</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;            covariance_matrix.coeffRef (1) = accu[1] - accu[6] * accu[7];</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;            covariance_matrix.coeffRef (2) = accu[2] - accu[6] * accu[8];</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;            covariance_matrix.coeffRef (4) = accu[3] - accu[7] * accu[7];</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;            covariance_matrix.coeffRef (5) = accu[4] - accu[7] * accu[8];</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;            covariance_matrix.coeffRef (8) = accu[5] - accu[8] * accu[8];</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;            covariance_matrix.coeffRef (3) = covariance_matrix.coeff (1);</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;            covariance_matrix.coeffRef (6) = covariance_matrix.coeff (2);</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;            covariance_matrix.coeffRef (7) = covariance_matrix.coeff (5);</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;          }</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160; </div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;          <span class="keywordflow">return</span> point_count;</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;        }</div>
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<a id="a965ac0738f571aae26a3f52ccbf26f8f"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a965ac0738f571aae26a3f52ccbf26f8f">&#9670;&nbsp;</a></span>generateCodeForBranchIndexComputation()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;class LabelDataType , class NodeType , class DataSet , class ExampleIndex &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator</a>&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;::generateCodeForBranchIndexComputation </td>
          <td>(</td>
          <td class="paramtype">NodeType &amp;&#160;</td>
          <td class="paramname">, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::ostream &amp;&#160;</td>
          <td class="paramname"><em>stream</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Generates code for branch index computation. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[out]</td><td class="paramname">stream</td><td>The destination for the generated code. </td></tr>
  </table>
  </dd>
</dl>

<p>实现了 <a class="el" href="classpcl_1_1_stats_estimator.html#a66add9942c6d8ae7b911574d5ac1fe19">pcl::StatsEstimator&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;</a>.</p>
<div class="fragment"><div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;        {</div>
<div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;          stream &lt;&lt; <span class="stringliteral">&quot;ERROR: RegressionVarianceStatsEstimator does not implement generateCodeForBranchIndex(...)&quot;</span>;</div>
<div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;        }</div>
</div><!-- fragment -->
</div>
</div>
<a id="a387f6dafb7ca51c20793fab106b202e1"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a387f6dafb7ca51c20793fab106b202e1">&#9670;&nbsp;</a></span>generateCodeForOutput()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;class LabelDataType , class NodeType , class DataSet , class ExampleIndex &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator</a>&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;::generateCodeForOutput </td>
          <td>(</td>
          <td class="paramtype">NodeType &amp;&#160;</td>
          <td class="paramname">, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::ostream &amp;&#160;</td>
          <td class="paramname"><em>stream</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Generates code for label output. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[out]</td><td class="paramname">stream</td><td>The destination for the generated code. </td></tr>
  </table>
  </dd>
</dl>

<p>实现了 <a class="el" href="classpcl_1_1_stats_estimator.html#a4c3efd505d6c8e711522a3d4e2da60fb">pcl::StatsEstimator&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;</a>.</p>
<div class="fragment"><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;        {</div>
<div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;          stream &lt;&lt; <span class="stringliteral">&quot;ERROR: RegressionVarianceStatsEstimator does not implement generateCodeForBranchIndex(...)&quot;</span>;</div>
<div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;        }</div>
</div><!-- fragment -->
</div>
</div>
<a id="af483f527511dd87173ff5c4dd130340e"></a>
<h2 class="memtitle"><span class="permalink"><a href="#af483f527511dd87173ff5c4dd130340e">&#9670;&nbsp;</a></span>getLabelOfNode()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;class LabelDataType , class NodeType , class DataSet , class ExampleIndex &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">LabelDataType <a class="el" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator</a>&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;::getLabelOfNode </td>
          <td>(</td>
          <td class="paramtype">NodeType &amp;&#160;</td>
          <td class="paramname"><em>node</em></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Returns the label of the specified node. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">node</td><td>The node which label is returned. </td></tr>
  </table>
  </dd>
</dl>

<p>实现了 <a class="el" href="classpcl_1_1_stats_estimator.html#a81eeb44edc44226e45e9559b8c6adf03">pcl::StatsEstimator&lt; LabelDataType, NodeType, DataSet, ExampleIndex &gt;</a>.</p>
<div class="fragment"><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;        {</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;          <span class="keywordflow">return</span> node.value;</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;        }</div>
</div><!-- fragment -->
</div>
</div>
<hr/>该类的文档由以下文件生成:<ul>
<li>recognition/include/pcl/recognition/face_detection/<a class="el" href="rf__face__utils_8h_source.html">rf_face_utils.h</a></li>
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